app.py

from flask import Flask, request, jsonify, render_template
import pickle
import numpy as np
import pandas as pd
#import matplotlib.pyplot as plt 
from sklearn.model_selection import train_test_split
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score


app = Flask(__name__)
 #model = []
#model = pickle.load(open("breastcancerusingvotingmechanism.pkl", "rb"))
#scaler = pickle.load(open("scaler.pkl", "rb"))

#"""LOADING DATA FROM CSV FILE"""

data=pd.read_csv("dataR2.csv")

#"""TO GET INFORMATION ABOUT VARIOUS PARAMETERS"""

data.describe()

data.head()

data['Classification'].value_counts()

X=data.drop(columns='Classification',axis=1)
Y=data['Classification']

#print(X)
#print(Y)

from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
scaler.fit(X)
standard_data=scaler.transform(X)

X=standard_data
Y=data['Classification']
X_train,X_test,Y_train,Y_test=train_test_split(X,Y,random_state=2,test_size=0.2)

# Initialize individual models
from sklearn.ensemble import RandomForestClassifier
model1 = LogisticRegression()
model2 = DecisionTreeClassifier()
model3 = SVC(probability=True)
model4 = RandomForestClassifier()

# Initialize voting classifier with soft voting
voting_clf = VotingClassifier(estimators=[('lr', model1), ('dt', model2), ('svm', model3),('rf',model4)], voting='soft')

# Train the voting classifier
voting_clf.fit(X_train, Y_train)

# Make predictions
y_pred = voting_clf.predict(X_test)

# Evaluate accuracy
accuracy = accuracy_score(Y_test, y_pred)
print("Accuracy:", accuracy)


#name of model is voting_clf
pickle.dump(voting_clf,open("breastcancerusingvotingmechanism.pkl","wb"))




@app.route("/")
def home():
    return render_template("cancerdata.html")


 
@app.route('/predict', methods=['POST'])
def Predict():
    #print(request)
    #print(request.args)
    # Extract form data
    age = float(request.form['age'])
    bmi = float(request.form['bmi'])
    glucose = float(request.form['glucose'])
    insulin = float(request.form['insulin'])
    homa = float(request.form['homa'])
    leptin = float(request.form['leptin'])
    adiponectin = float(request.form['adiponectin'])
    resistin = float(request.form['resistin'])
    mcp1 = float(request.form['mcp1'])
    #   print(age)
    #  print(mcp1)


    # Prepare input data
    input_data = np.array([[age, bmi, glucose, insulin, homa, leptin, adiponectin, resistin, mcp1]])

    #print(input_data)    

    # changing the input_data to numpy array
    input_data_as_numpy_array = np.asarray(input_data)

    # reshape the array as we are predicting for one instance
    input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)

    # standardize the input data
    std_data = scaler.transform(input_data_reshaped)
    print(std_data)

    prediction = voting_clf.predict(std_data)
    print(prediction)

    
    # Interpret prediction
    if prediction[0] == 0:
        result = 'The person does not have Breast Cancer'
    else:
        result = 'The person has Breast Cancer'

    #name of model is voting_clf
    pickle.dump(voting_clf,open("breastcancerusingvotingmechanism.pkl","wb"))


    # Return prediction result as JSON

    return jsonify({'prediction_text': result})

if __name__ == "__main__":
    app.run(debug=True)